Method of propulsor management in electric aircraft

ABSTRACT

A system for propulsion management of an electric aircraft. The system includes an electric aircraft that is configured to transition between a hover state and a fixed-wing flight state. The electric aircraft includes at least one set of a plurality of propulsors that are coupled to the electric aircraft. The system includes a flight controller that is coupled to the electric aircraft. The flight controller is configured to detect a state transition of the electric aircraft from the hover state to the fixed-wing flight state. The flight controller is configured to send a parking or unparking command to the at least one set of a plurality of propulsors to reduce air drag.

FIELD OF THE INVENTION

The present invention generally relates to the field of electricaircraft. In particular, the present invention is directed to a systemand method for propulsor management for an electric aircraft.

BACKGROUND

Modern electric aircraft, such as vertical landing and takeoff aircraft(eVTOL) may include a set of vertical propulsors. These verticalpropulsors may be stationary and not in use during edgewise flight. Assuch, they may increase the air resistance and/or drag on the electricaircraft.

SUMMARY OF THE DISCLOSURE

In an aspect, a system for propulsion management of an electric aircraftincludes an electric aircraft. The electric aircraft is configured totransition between a hover state and a fixed-wing flight state. Theelectric aircraft includes at least one set of a plurality ofpropulsors. The at least one set of a plurality of propulsors is coupledto the electric aircraft. In some embodiments, the system includes aflight controller coupled to the electric aircraft. The flightcontroller is configured to detect a state transition of the electricaircraft from the hover state to the fixed-wing flight state. In someembodiments, the flight controller is configured to send a parkingcommand to the at least one set of a plurality of propulsors.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a front view of an exemplary embodiment of an electricaircraft;

FIG. 2 is front view of an exemplary embodiment of a block diagram of asystem of management for propulsors of an electric aircraft;

FIG. 3 is a front view of an exemplary embodiment of multiple axis of anelectric aircraft;

FIG. 4 is a block diagram of an exemplary embodiment of a flightcontroller system;

FIG. 5 is a flowchart of an exemplary embodiment of a method of managingpropulsors of an electric aircraft;

FIG. 6 is a is a block diagram of an exemplary embodiment of a computingsystem; and

FIG. 7 is an exemplary embodiment of a machine learning system.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however,that the present invention may be practiced without these specificdetails. As used herein, the word “exemplary” or “illustrative” means“serving as an example, instance, or illustration.” Any implementationdescribed herein as “exemplary” or “illustrative” is not necessarily tobe construed as preferred or advantageous over other implementations.All of the implementations described below are exemplary implementationsprovided to enable persons skilled in the art to make or use theembodiments of the disclosure and are not intended to limit the scope ofthe disclosure, which is defined by the claims.

Described herein is a system for propulsion management of an electricaircraft. In some embodiments, the system may include an electricaircraft. The electric aircraft may be configured to transition betweena hover state and a fixed-wing flight state. In some embodiments, theelectric aircraft may include at least one set of a plurality ofpropulsors. The at least one set of a plurality of propulsors may becoupled to the electric aircraft. In some embodiments, the system mayinclude a flight controller coupled to the electric aircraft. The flightcontroller may be configured to detect a state transition of theelectric aircraft from the hover state to the fixed-wing flight state.In some embodiments, the flight controller may be configured to send aparking command to the at least one set of a plurality of propulsors.

Described herein is a method of propulsor management of an electricaircraft. In some embodiments, the method may include selecting anelectric aircraft configured to transition between a hover state and afixed-wing flight state. In some embodiments, the electric aircraft mayinclude at least one set of a plurality of propulsors coupled to theelectric aircraft. In some embodiments, the method may includedetermining a state of the electric aircraft via a flight controller. Insome embodiments, the flight controller may be configured to detect astate transition of the electric aircraft. In some embodiments, themethod may include sending, via the flight controller, a parking commandto the at least one set of a plurality of propulsors. In someembodiments, the method may include moving, via at least a first set ofinverters, at least one propulsor of the at least one set of propulsorsinto a parked position.

Referring now to FIG. 1, an illustration of an exemplary embodiment ofan electric aircraft 100 is shown. Electric aircraft 100 may include avertical takeoff and landing aircraft (eVTOL). As used herein, avertical take-off and landing (eVTOL) aircraft is an electricallypowered aircraft that can take off and land vertically; eVTOL aircraftmay be capable of hovering. In order without limitation to optimizepower and energy necessary to propel an eVTOL or to increasemaneuverability, the eVTOL may be capable of rotor-based cruisingflight, rotor-based takeoff, rotor-based landing, fixed-wing cruisingflight, airplane-style takeoff, airplane-style landing, and/or anycombination thereof. Rotor-based flight, as described herein, is wherethe aircraft generated lift and propulsion by way of one or more poweredrotors coupled with an engine, such as a “quad copter,” helicopter, orother vehicle that maintains its lift primarily using downward thrustingpropulsors. Fixed-wing flight, as described herein, flight using wingsand/or foils that generate life caused by an aircraft's forward airspeedand the shape of the wings and/or foils, such as in airplane-styleflight.

With continued reference to FIG. 1, a number of aerodynamic forces mayact upon electric aircraft 100 during flight. Forces acting on anelectric aircraft 100 during flight may include, without limitation,thrust, a forward force produced by a propulsor of electric aircraft100, which may acts parallel to a longitudinal axis of the aircraft.Another force acting upon electric aircraft 100 may include, withoutlimitation, drag, defined as a rearward retarding force which is causedby disruption of airflow by any protruding surface of electric aircraft100 such as, without limitation, a wing, rotor, and/or fuselage. Dragmay oppose thrust and act rearward parallel to relative wind. A furtherforce acting upon electric aircraft 100 may include, without limitation,weight, which may include a combined load of the electric aircraft 100itself, crew, baggage, and/or fuel. Weight may pull electric aircraft100 downward due to the force of gravity. An additional force acting onelectric aircraft 100 may include, without limitation, lift, which mayact to oppose the downward force of weight and may be produced by adynamic effect of air acting on an airfoil and/or downward thrust from apropulsor of the electric aircraft. Lift generated by an airfoil maydepend on speed of airflow, density of air, total area of the airfoiland/or a segment thereof, and/or an angle of attack between air and theairfoil. In a non-limiting example, electric aircraft 100 may bedesigned to be as lightweight as possible. Reducing weight of anaircraft and designing to reduce a number of components may optimizeweight. To save energy, it may be useful to reduce weight of componentsof an electric aircraft 100, including without limitation propulsorsand/or propulsion assemblies.

Referring still to FIG. 1, electric aircraft 100 may include at least avertical propulsor 104 and at least a forward propulsor 108. At least aforward propulsor 108 as used in this disclosure is a propulsorpositioned for propelling an aircraft in a “forward” direction; at leasta forward propulsor may include one or more propulsors mounted on thefront, on the wings, at the rear, or a combination of any suchpositions. At least a forward propulsor may propel an aircraft forwardfor fixed-wing and/or “airplane”-style flight, takeoff, and/or landing,and/or may propel the aircraft forward or backward on the ground. Atleast a vertical propulsor 104 and at least a forward propulsor 108includes a thrust element. At least a thrust element may include anydevice or component that converts the mechanical energy of a motor, forinstance in the form of rotational motion of a shaft, into thrust in afluid medium. At least a thrust element may include, without limitation,a device using moving or rotating foils, including without limitationone or more rotors, an airscrew or propeller, a set of airscrews orpropellers such as contrarotating propellers, a moving or flapping wing,or the like. At least a thrust element may include without limitation amarine propeller or screw, an impeller, a turbine, a pump-jet, a paddleor paddle-based device, or the like. As another non-limiting example, atleast a thrust element may include an eight-bladed pusher propeller,such as an eight-bladed propeller mounted behind the engine to ensurethe drive shaft is in compression. Propulsors may include at least amotor mechanically coupled to the at least a first propulsor as a sourceof thrust. A motor may include without limitation, any electric motor,where an electric motor is a device that converts electrical energy intomechanical energy, for instance by causing a shaft to rotate. At least amotor may be driven by direct current (DC) electric power; for instance,at least a first motor may include a brushed DC at least a first motor,or the like. At least a first motor may be driven by electric powerhaving varying or reversing voltage levels, such as alternating current(AC) power as produced by an alternating current generator and/orinverter, or otherwise varying power, such as produced by a switchingpower source. At least a first motor may include, without limitation,brushless DC electric motors, permanent magnet synchronous at least afirst motor, switched reluctance motors, or induction motors. Inaddition to inverter and/or a switching power source, a circuit drivingat least a first motor may include electronic speed controllers or othercomponents for regulating motor speed, rotation direction, and/ordynamic braking. Persons skilled in the art, upon reviewing the entiretyof this disclosure, will be aware of various devices that may be used asat least a thrust element.

With continued reference to FIG. 1, a “vertical propulsor” as used inthis disclosure is a propulsor that propels an aircraft in an upwarddirection; one or more vertical propulsors may be mounted on the front,on the wings, at the rear, and/or any suitable location. A “propulsor,”as used in this disclosure, is a component or device used to propel acraft by exerting force on a fluid medium, which may include a gaseousmedium such as air or a liquid medium such as water. At least a verticalpropulsor 104 may generate a substantially downward thrust, tending topropel an aircraft in a vertical direction providing thrust formaneuvers such as without limitation, vertical take-off, verticallanding, hovering, and/or rotor-based flight such as “quadcopter” orsimilar styles of flight. Vertical propulsors 104 may have a horizontalaxis 112. In some embodiments, vertical propulsors 104 may have avertical axis 116. In some embodiments, horizontal axis 112 may be aminimum drag axis. A “minimum drag axis,” as defined in this disclosure,is an axis that, when aligned with a direction of airflow and/orrelative wind at a propulsor 104, produce a minimal amount of drag onthe vertical propulsor 104, as described in further detail below.

Referring now to FIG. 2, an exemplary embodiment of a propulsormanagement system 200 is illustrated. System 200 may include an electricaircraft 204. In some embodiments, electric aircraft 204 may be aneVTOL. In some embodiments, electric aircraft 204 may have one or morestates of operation. Electric aircraft 204 may have a hover state 208.In hover state 208, electric aircraft 204 may be moving through the airalong a vertical path. Electric aircraft 204 may utilize propulsors 224a-b to generate a lift. As described above in FIG. 1, in someembodiments, electric aircraft may be in hover state 208 during liftoffoperations. In another embodiment, electric aircraft 202 may be in hoverstate 204 in landing operations. In other embodiments, hover state 208may be when electric aircraft 204 maintains an altitude when airborne.Electric aircraft 204 may use propulsors 224 a-b to achieve ascent anddescent in hover state 208. As described in FIG. 1, in some embodiments,electric aircraft 204 may have a fixed-wing flight state 212. Electricaircraft 204 may be in fixed-wing flight state 212 during forward,backward, and sideways propulsion. Fixed-wing flight state 212 mayinclude edgewise flight. In some embodiments, electric aircraft 204 mayhave a first set of propulsors 224A. First set of propulsors 224A may beused in hover state 204. In other embodiments, electric aircraft 204 mayhave a second set of propulsors 224B for fixed-wing flight state 212. Insome embodiments, electric aircraft 204 may use the same set ofpropulsors for both hover state 204 and fixed-wing flight state 206.System 200 may include a flight controller 216.

Still referring to FIG. 2, flight controller 216 may communicate with atleast a sensor 232. In some embodiments, sensor 232 may be configured todetect a plurality of flight operations of electric aircraft 204. Insome embodiments, sensor 232 may detect a change of electric aircraft204 during a transition of electric aircraft 204 between fixed-wingflight state 212 and hover state 208. In some embodiments, sensor 232may include one or more motion sensors, which may include any elementsuitable for use as an inertial measurement unit (IMU) or any componentthereof, including without limitation one or more accelerometers, one ormore gyroscopes, one or more magnetometers, or the like. Motion sensorsmay be selected to detect motion in three directions spanning threedimensions for instance, a set of three accelerometers may be configuredor arranged to detect acceleration in three directions spanning threedimensions, such as three orthogonal directions, or three gyroscopes maybe configured to detect changes in pitch spanning three dimensions, suchas may be achieved by three mutually orthogonal gyroscopes. Sensor 232may include one or more environmental sensors, including withoutlimitation sensors for detecting wind, speed, temperature, or the like.In some embodiments, sensor 232 may include an altimeter. In someembodiments, sensor 232 may be configured to measure physical and/orelectrical parameters, such as without limitation temperature and/orvoltage, of electric aircraft 208 and propulsors 224 a-b. Sensor 232and/or a control circuit incorporated therein and/or communicativelyconnected thereto, may further be configured to detect voltage andcurrent of a first and second set of inverters 228 a-b. Detection may beperformed using any suitable component, set of components, and/ormechanism for direct or indirect measurement and/or detection of voltagelevels, including without limitation comparators, analog to digitalconverters, any form of voltmeter, or the like.

Outputs from sensor 232 or any other component present within system maybe analog or digital. Onboard or remotely located processors can convertthose output signals from sensor suite to a usable form by thedestination of those signals. The usable form of output signals fromsensors, through processor may be either digital, analog, a combinationthereof or an otherwise unstated form. Processing may be configured totrim, offset, or otherwise compensate the outputs of sensor suite. Basedon sensor output, the processor can determine the output to send todownstream component. Processor can include signal amplification,operational amplifier (OpAmp), filter, digital/analog conversion,linearization circuit, current-voltage change circuits, resistancechange circuits such as Wheatstone Bridge, an error compensator circuit,a combination thereof or otherwise undisclosed components.

With continued reference to FIG. 2, system 200 may have at least a firstset of inverters 228A. At least a first set of inverters 228A may beconfigured to operably move the propulsors 224A-B. At least a first setof inverters 228A may be coupled to propulsors 224A-B. In someembodiments, at least a first set of inverters 228A may include aplurality of components to apply a torque to propulsors 224A-B. In someembodiments, at least a first set of inverters 228A may include aplurality of inverters that may be configured to transform DC power toAC power. The AC power may be used to drive the motor by adjusting thefrequency and voltage supplied to the motor. In some embodiments, atleast a first set of inverters 210 may be configured to output between100 and 300 kwh of electrical power to a propulsor of propulsors 212. Insome embodiments, at least a first set of inverters 210 may beconfigured to output 200 kwh of electrical power to a propulsor ofpropulsors 212. The inverter may be entirely electronic or a combinationof mechanical elements and electronic circuitry. The invertor may allowfor variable speed and torque of the motor based on the demands of thevehicle. In some embodiments, inverters of at least a first set ofinverters 228A may include a plurality of wires. The plurality of wiresmay be wound around one or more stators of propulsors 224 a-b. Theplurality of wires may have multiple windings around one or more statorsof propulsors 224 a-b. In some embodiments, each winding of theplurality of wires may be connected to a different inverter. Flightcontroller 216 may be configured to communicate data to and from the atleast a first set of inverters 210. In some embodiments, flightcontroller 208 may communicate data to and from the at least a first setof inverters 210 wirelessly. In other embodiments, flight controller 216may communicate data to and at least a first set of inverters 228A via awired connection. In some embodiments, flight controller 216 may beconfigured to send commands to at least a first set of inverters 228A.In some embodiments, flight controller 216 may send a command to atleast a first set of inverters 228A to apply a torque to at least onepropulsor of propulsors 224 a-b of electric aircraft 204. In someembodiments, flight controller 216 may be configured to send commands toa second set of inverters 228B to apply a torque to at least onepropulsor of propulsors 224 a-b. In some embodiments, flight controller216 may send a command to inverters 224 a-b to hold at least onepropulsor of the propulsors 224 a-b in a fixed position.

In some embodiments, and with continued reference to FIG. 2, flightcontroller 216 may send a command to at least a first set of inverters228A to apply a clockwise torque to propulsors 224 a-b. In otherembodiments, flight controller 216 may send a command to at least afirst set of inverters 228A to apply a counter-clockwise torque to atleast one propulsor of propulsors 224 a-b of electric aircraft 204. Insome embodiments, flight controller 216 may send a command to at least afirst set of inverters 228A to apply zero torque to propulsors 224 a-b.In some embodiments, propulsor management system 200 may have a secondset of inverters 228B. Flight controller 216 may send a command to thesecond set of inverters 228B to hold at least one propulsor of thepropulsors 224 a-b in a stationary position. In some embodiments, secondset of inverters 228B may be configured to generate a magnetic field. Insome embodiments, second set of inverters 228B may include multiplewindings of a conductive wire. The windings may have a current runningthrough them that may induce a magnetic field. Inverters 228B may use amagnetic field to hold propulsors 224 a-b in a static position. In someembodiments, flight controller 216 may send commands to at least a firstset of inverters 228A to move propulsors 224 a-b within a tolerancerange in deviation from an axis. In some embodiments, the tolerancerange may be +2 degrees. In other embodiments, the tolerance range maybe −2 degrees. In yet other embodiments, the tolerance range may bebetween −10 degrees to +10 degrees. In other embodiments, the tolerancemay be greater than or less than between −10 degrees to +10 degrees. Insome embodiments, the tolerance range may be determined based on batterylevels of electric aircraft 204. In other embodiments, the tolerancerange may be determined based on heating caused by the parking andunparking of propulsors 224 a-b.

Further referring to FIG. 2, flight controller 216 may be configured todetect a transition of electric aircraft 200 between hover state 208 andfixed-wing flight state 212. In some embodiments, flight controller 216may be configured to receive data from sensor 232. As described above,sensor 232 may detect a change of a variety of parameters of electricaircraft 204. Sensor 232 may detect a change in motion, wind speed, winddirection, altitude, or other parameter changes. Sensor 232 may senddata to flight controller 216 communicating a change in a parameter ofelectric aircraft 204. Flight controller 216 may process the datareceived from sensor 232. Flight controller 216 may determine thatelectric aircraft 204 should transition from hover state 208 tofixed-wing flight state 212 and vice versa. In some embodiments, flightcontroller 216 may determine a state transition is needed based on thedata from sensor 232. In other embodiments, flight controller 216 maydetermine that a state transition is needed based on a flight plan. Inother embodiments, flight controller 216 may determine that a statetransition is needed based on detection of a plurality of flightconditions, such as stall. In some embodiments, flight controller 216may determine a state transition is need based on an automateddecision-making process. The automated decision-making process mayinclude artificial intelligence and machine learning.

Still referring to FIG. 2, flight controller 216 may be configured tosend a plurality of commands to at least a first set of inverters 228Aupon detection of a transition between hover state 208 and fixed-wingstate 212. In some embodiments, flight controller 216 may detect thatelectric aircraft 204 is in hover state 208. Flight controller 216 maysend a command to at least a first set of inverters 228A to positionpropulsors 224 a-b in a position configured for vertical aerialmaneuvers. In some embodiments, flight controller 216 may send a commandto at least a first set of inverters 228A to enable propulsors 224A tofreely rotate. In hover state 208, propulsors 224 a-b may generate liftto move electric aircraft 204 along a vertical path. Flight controller216 may detect electric aircraft 204 in fixed-wing flight state 212. Insome embodiments, in fixed-wing flight state 212, flight controller 216may send a command to second set of inverters 228B to hold a set ofpropulsors of propulsors 224 a-b in a position to reduce air resistanceacross propulsors 224 a-b. A minimum drag axis of each propulsor ofplurality of propulsors 224 a-b may be aligned along a longitudinal axisof electric aircraft 204 as described below in FIG. 3. In otherembodiments, flight controller 216 may determine a minimal drag axis ofelectric aircraft 212. Drag may oppose thrust and act rearward parallelto relative wind. In some embodiments, the minimal drag axis may be apath along electric aircraft 204 that may reduce drag across electricaircraft 204. In some embodiments, the minimal drag axis may align witha distal end of a first blade of electric aircraft 204 to a distal endof a second blade of electric aircraft 204, as described above inFIG. 1. In other embodiments, flight controller 208 may determine aminimal drag axis based on surrounding airflow of electric aircraft 212.The surrounding airflow may include relative wind of electric aircraft204. Relative wind may be a direction of movement of the atmosphererelative to electric aircraft 204. In some embodiments, relative windmay be in opposite direction to a movement of electric aircraft 204.Flight controller 208 may send a command to at least a first set ofinverters 210 to position propulsors 212 to point towards a direction ofsurrounding airflow in order to reduce drag.

Now referring to FIG. 3, an exemplary embodiment of a plurality of axisof an electric aircraft is presented. Electric aircraft 300 may have atransverse axis 304. Transverse axis 304 may have an origin at a centerof gravity of electric aircraft 300. Transverse axis 304 may be aparallel to a line drawn from one distal end of a wingtip to anotheropposing distal end of a second wingtip. In some embodiments, motionabout transverse axis 304 may be referred to as pitch. Electric aircraft300 may have longitudinal axis 308. In some embodiments, longitudinalaxis 308 may be referred to as the pitch axis. In some embodiments,longitudinal axis 308 may have an origin at a center of gravity ofelectric aircraft 300. In some embodiments, longitudinal axis 308 may bedirected forward, parallel to a fuselage reference line. In someembodiments, motion about longitudinal axis 308 may be referred to asroll. In some embodiments, angular displacement about longitudinal axis304 may be referred to as bank. Electric aircraft 300 may have verticalaxis 312. In some embodiments, vertical axis 312 may have a center ofgravity at electric aircraft 300. In some embodiments, vertical axis 312may be directed towards the top and/or bottom of electric aircraft 300.In some embodiments, vertical axis 312 may be referred to as the yawaxis. In some embodiments, vertical axis 312 may be perpendicular to thewings of electric aircraft 300. Motion about vertical axis 312 may bereferred to as yaw.

Now referring to FIG. 4, an exemplary embodiment 400 of a flightcontroller 404 is illustrated. As used in this disclosure a “flightcontroller” is a computing device of a plurality of computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and flight instruction. Flight controller 404 may includeand/or communicate with any computing device as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Further, flight controller 404may include a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. In embodiments, flight controller 404 may be installed in anaircraft, may control the aircraft remotely, and/or may include anelement installed in the aircraft and a remote element in communicationtherewith.

In an embodiment, and still referring to FIG. 4, flight controller 404may include a signal transformation component 408. As used in thisdisclosure a “signal transformation component” is a component thattransforms and/or converts a first signal to a second signal, wherein asignal may include one or more digital and/or analog signals. Forexample, and without limitation, signal transformation component 408 maybe configured to perform one or more operations such as preprocessing,lexical analysis, parsing, semantic analysis, and the like thereof. Inan embodiment, and without limitation, signal transformation component408 may include one or more analog-to-digital convertors that transforma first signal of an analog signal to a second signal of a digitalsignal. For example, and without limitation, an analog-to-digitalconverter may convert an analog input signal to a 10-bit binary digitalrepresentation of that signal. In another embodiment, signaltransformation component 408 may include transforming one or morelow-level languages such as, but not limited to, machine languagesand/or assembly languages. For example, and without limitation, signaltransformation component 408 may include transforming a binary languagesignal to an assembly language signal. In an embodiment, and withoutlimitation, signal transformation component 408 may include transformingone or more high-level languages and/or formal languages such as but notlimited to alphabets, strings, and/or languages. For example, andwithout limitation, high-level languages may include one or more systemlanguages, scripting languages, domain-specific languages, visuallanguages, esoteric languages, and the like thereof. As a furthernon-limiting example, high-level languages may include one or morealgebraic formula languages, business data languages, string and listlanguages, object-oriented languages, and the like thereof.

Still referring to FIG. 4, signal transformation component 408 may beconfigured to optimize an intermediate representation 412. As used inthis disclosure an “intermediate representation” is a data structureand/or code that represents the input signal. Signal transformationcomponent 408 may optimize intermediate representation as a function ofa data-flow analysis, dependence analysis, alias analysis, pointeranalysis, escape analysis, and the like thereof. In an embodiment, andwithout limitation, signal transformation component 408 may optimizeintermediate representation 412 as a function of one or more inlineexpansions, dead code eliminations, constant propagation, looptransformations, and/or automatic parallelization functions. In anotherembodiment, signal transformation component 408 may optimizeintermediate representation as a function of a machine dependentoptimization such as a peephole optimization, wherein a peepholeoptimization may rewrite short sequences of code into more efficientsequences of code. Signal transformation component 408 may optimizeintermediate representation to generate an output language, wherein an“output language,” as used herein, is the native machine language offlight controller 404. For example, and without limitation, nativemachine language may include one or more binary and/or numericallanguages.

In an embodiment, and without limitation, signal transformationcomponent 408 may include transform one or more inputs and outputs as afunction of an error correction code. An error correction code, alsoknown as error correcting code (ECC), is an encoding of a message or lotof data using redundant information, permitting recovery of corrupteddata. An ECC may include a block code, in which information is encodedon fixed-size packets and/or blocks of data elements such as symbols ofpredetermined size, bits, or the like. Reed-Solomon coding, in whichmessage symbols within a symbol set having q symbols are encoded ascoefficients of a polynomial of degree less than or equal to a naturalnumber k, over a finite field F with q elements; strings so encoded havea minimum hamming distance of k+1, and permit correction of (q−k−1)/2erroneous symbols. Block code may alternatively or additionally beimplemented using Golay coding, also known as binary Golay coding,Bose-Chaudhuri, Hocquenghuem (BCH) coding, multidimensional parity-checkcoding, and/or Hamming codes. An ECC may alternatively or additionallybe based on a convolutional code.

In an embodiment, and still referring to FIG. 4, flight controller 404may include a reconfigurable hardware platform 416. A “reconfigurablehardware platform,” as used herein, is a component and/or unit ofhardware that may be reprogrammed, such that, for instance, a data pathbetween elements such as logic gates or other digital circuit elementsmay be modified to change an algorithm, state, logical sequence, or thelike of the component and/or unit. This may be accomplished with suchflexible high-speed computing fabrics as field-programmable gate arrays(FPGAs), which may include a grid of interconnected logic gates,connections between which may be severed and/or restored to program inmodified logic. Reconfigurable hardware platform 416 may be reconfiguredto enact any algorithm and/or algorithm selection process received fromanother computing device and/or created using machine-learningprocesses.

Still referring to FIG. 4, reconfigurable hardware platform 416 mayinclude a logic component 420. As used in this disclosure a “logiccomponent” is a component that executes instructions on output language.For example, and without limitation, logic component may perform basicarithmetic, logic, controlling, input/output operations, and the likethereof. Logic component 420 may include any suitable processor, such aswithout limitation a component incorporating logical circuitry forperforming arithmetic and logical operations, such as an arithmetic andlogic unit (ALU), which may be regulated with a state machine anddirected by operational inputs from memory and/or sensors; logiccomponent 420 may be organized according to Von Neumann and/or Harvardarchitecture as a non-limiting example. Logic component 420 may include,incorporate, and/or be incorporated in, without limitation, amicrocontroller, microprocessor, digital signal processor (DSP), FieldProgrammable Gate Array (FPGA), Complex Programmable Logic Device(CPLD), Graphical Processing Unit (GPU), general purpose GPU, TensorProcessing Unit (TPU), analog or mixed signal processor, TrustedPlatform Module (TPM), a floating-point unit (FPU), and/or system on achip (SoC). In an embodiment, logic component 420 may include one ormore integrated circuit microprocessors, which may contain one or morecentral processing units, central processors, and/or main processors, ona single metal-oxide-semiconductor chip. Logic component 420 may beconfigured to execute a sequence of stored instructions to be performedon the output language and/or intermediate representation 412. Logiccomponent 420 may be configured to fetch and/or retrieve the instructionfrom a memory cache, wherein a “memory cache,” as used in thisdisclosure, is a stored instruction set on flight controller 404. Logiccomponent 420 may be configured to decode the instruction retrieved fromthe memory cache to opcodes and/or operands. Logic component 420 may beconfigured to execute the instruction on intermediate representation 412and/or output language. For example, and without limitation, logiccomponent 420 may be configured to execute an addition operation onintermediate representation 412 and/or output language.

In an embodiment, and without limitation, logic component 420 may beconfigured to calculate a flight element 424. As used in this disclosurea “flight element” is an element of datum denoting a relative status ofaircraft. For example, and without limitation, flight element 424 maydenote one or more torques, thrusts, airspeed velocities, forces,altitudes, groundspeed velocities, directions during flight, directionsfacing, forces, orientations, and the like thereof. For example, andwithout limitation, flight element 424 may denote that aircraft iscruising at an altitude and/or with a sufficient magnitude of forwardthrust. As a further non-limiting example, flight status may denote thatis building thrust and/or groundspeed velocity in preparation for atakeoff. As a further non-limiting example, flight element 424 maydenote that aircraft is following a flight path accurately and/orsufficiently.

Still referring to FIG. 4, flight controller 404 may include a chipsetcomponent 428. As used in this disclosure a “chipset component” is acomponent that manages data flow. In an embodiment, and withoutlimitation, chipset component 428 may include a northbridge data flowpath, wherein the northbridge dataflow path may manage data flow fromlogic component 420 to a high-speed device and/or component, such as aRAM, graphics controller, and the like thereof. In another embodiment,and without limitation, chipset component 428 may include a southbridgedata flow path, wherein the southbridge dataflow path may manage dataflow from logic component 420 to lower-speed peripheral buses, such as aperipheral component interconnect (PCI), industry standard architecture(ICA), and the like thereof. In an embodiment, and without limitation,southbridge data flow path may include managing data flow betweenperipheral connections such as ethernet, USB, audio devices, and thelike thereof. Additionally or alternatively, chipset component 428 maymanage data flow between logic component 420, memory cache, and a flightcomponent 432. As used in this disclosure a “flight component” is aportion of an aircraft that can be moved or adjusted to affect one ormore flight elements. For example, flight component 432 may include acomponent used to affect the aircrafts' roll and pitch which maycomprise one or more ailerons. As a further example, flight component432 may include a rudder to control yaw of an aircraft. In anembodiment, chipset component 428 may be configured to communicate witha plurality of flight components as a function of flight element 424.For example, and without limitation, chipset component 428 may transmitto an aircraft rotor to reduce torque of a first lift propulsor andincrease the forward thrust produced by a pusher component to perform aflight maneuver.

In an embodiment, and still referring to FIG. 4, flight controller 404may be configured generate an autonomous function. As used in thisdisclosure an “autonomous function” is a mode and/or function of flightcontroller 404 that controls aircraft automatically. For example, andwithout limitation, autonomous function may perform one or more aircraftmaneuvers, take offs, landings, altitude adjustments, flight levelingadjustments, turns, climbs, and/or descents. As a further non-limitingexample, autonomous function may adjust one or more airspeed velocities,thrusts, torques, and/or groundspeed velocities. As a furthernon-limiting example, autonomous function may perform one or more flightpath corrections and/or flight path modifications as a function offlight element 424. In an embodiment, autonomous function may includeone or more modes of autonomy such as, but not limited to, autonomousmode, semi-autonomous mode, and/or non-autonomous mode. As used in thisdisclosure “autonomous mode” is a mode that automatically adjusts and/orcontrols aircraft and/or the maneuvers of aircraft in its entirety. Forexample, autonomous mode may denote that flight controller 404 willadjust the aircraft. As used in this disclosure a “semi-autonomous mode”is a mode that automatically adjusts and/or controls a portion and/orsection of aircraft. For example, and without limitation,semi-autonomous mode may denote that a pilot will control thepropulsors, wherein flight controller 404 will control the aileronsand/or rudders. As used in this disclosure “non-autonomous mode” is amode that denotes a pilot will control aircraft and/or maneuvers ofaircraft in its entirety.

In an embodiment, and still referring to FIG. 4, flight controller 404may generate autonomous function as a function of an autonomousmachine-learning model. As used in this disclosure an “autonomousmachine-learning model” is a machine-learning model to produce anautonomous function output given flight element 424 and a pilot signal436 as inputs; this is in contrast to a non-machine learning softwareprogram where the commands to be executed are determined in advance by auser and written in a programming language. As used in this disclosure a“pilot signal” is an element of datum representing one or more functionsa pilot is controlling and/or adjusting. For example, pilot signal 436may denote that a pilot is controlling and/or maneuvering ailerons,wherein the pilot is not in control of the rudders and/or propulsors. Inan embodiment, pilot signal 436 may include an implicit signal and/or anexplicit signal. For example, and without limitation, pilot signal 436may include an explicit signal, wherein the pilot explicitly statesthere is a lack of control and/or desire for autonomous function. As afurther non-limiting example, pilot signal 436 may include an explicitsignal directing flight controller 404 to control and/or maintain aportion of aircraft, a portion of the flight plan, the entire aircraft,and/or the entire flight plan. As a further non-limiting example, pilotsignal 436 may include an implicit signal, wherein flight controller 404detects a lack of control such as by a malfunction, torque alteration,flight path deviation, and the like thereof. In an embodiment, andwithout limitation, pilot signal 436 may include one or more explicitsignals to reduce torque, and/or one or more implicit signals thattorque may be reduced due to reduction of airspeed velocity. In anembodiment, and without limitation, pilot signal 436 may include one ormore local and/or global signals. For example, and without limitation,pilot signal 436 may include a local signal that is transmitted by apilot and/or crew member. As a further non-limiting example, pilotsignal 436 may include a global signal that is transmitted by airtraffic control and/or one or more remote users that are incommunication with the pilot of aircraft. In an embodiment, pilot signal436 may be received as a function of a tri-state bus and/or multiplexorthat denotes an explicit pilot signal should be transmitted prior to anyimplicit or global pilot signal.

Still referring to FIG. 4, autonomous machine-learning model may includeone or more autonomous machine-learning processes such as supervised,unsupervised, or reinforcement machine-learning processes that flightcontroller 404 and/or a remote device may or may not use in thegeneration of autonomous function. As used in this disclosure “remotedevice” is an external device to flight controller 404. Additionally oralternatively, autonomous machine-learning model may include one or moreautonomous machine-learning processes that a field-programmable gatearray (FPGA) may or may not use in the generation of autonomousfunction. Autonomous machine-learning process may include, withoutlimitation machine learning processes such as simple linear regression,multiple linear regression, polynomial regression, support vectorregression, ridge regression, lasso regression, elasticnet regression,decision tree regression, random forest regression, logistic regression,logistic classification, K-nearest neighbors, support vector machines,kernel support vector machines, naïve bayes, decision treeclassification, random forest classification, K-means clustering,hierarchical clustering, dimensionality reduction, principal componentanalysis, linear discriminant analysis, kernel principal componentanalysis, Q-learning, State Action Reward State Action (SARSA), Deep-Qnetwork, Markov decision processes, Deep Deterministic Policy Gradient(DDPG), or the like thereof.

In an embodiment, and still referring to FIG. 4, autonomous machinelearning model may be trained as a function of autonomous training data,wherein autonomous training data may correlate a flight element, pilotsignal, and/or simulation data to an autonomous function. For example,and without limitation, a flight element of an airspeed velocity, apilot signal of limited and/or no control of propulsors, and asimulation data of required airspeed velocity to reach the destinationmay result in an autonomous function that includes a semi-autonomousmode to increase thrust of the propulsors. Autonomous training data maybe received as a function of user-entered valuations of flight elements,pilot signals, simulation data, and/or autonomous functions. Flightcontroller 404 may receive autonomous training data by receivingcorrelations of flight element, pilot signal, and/or simulation data toan autonomous function that were previously received and/or determinedduring a previous iteration of generation of autonomous function.Autonomous training data may be received by one or more remote devicesand/or FPGAs that at least correlate a flight element, pilot signal,and/or simulation data to an autonomous function. Autonomous trainingdata may be received in the form of one or more user-enteredcorrelations of a flight element, pilot signal, and/or simulation datato an autonomous function.

Still referring to FIG. 4, flight controller 404 may receive autonomousmachine-learning model from a remote device and/or FPGA that utilizesone or more autonomous machine learning processes, wherein a remotedevice and an FPGA is described above in detail. For example, andwithout limitation, a remote device may include a computing device,external device, processor, FPGA, microprocessor and the like thereof.Remote device and/or FPGA may perform the autonomous machine-learningprocess using autonomous training data to generate autonomous functionand transmit the output to flight controller 404. Remote device and/orFPGA may transmit a signal, bit, datum, or parameter to flightcontroller 404 that at least relates to autonomous function.Additionally or alternatively, the remote device and/or FPGA may providean updated machine-learning model. For example, and without limitation,an updated machine-learning model may be comprised of a firmware update,a software update, an autonomous machine-learning process correction,and the like thereof. As a non-limiting example a software update mayincorporate a new simulation data that relates to a modified flightelement. Additionally or alternatively, the updated machine learningmodel may be transmitted to the remote device and/or FPGA, wherein theremote device and/or FPGA may replace the autonomous machine-learningmodel with the updated machine-learning model and generate theautonomous function as a function of the flight element, pilot signal,and/or simulation data using the updated machine-learning model. Theupdated machine-learning model may be transmitted by the remote deviceand/or FPGA and received by flight controller 404 as a software update,firmware update, or corrected habit machine-learning model. For example,and without limitation autonomous machine learning model may utilize aneural net machine-learning process, wherein the updatedmachine-learning model may incorporate a gradient boostingmachine-learning process.

Still referring to FIG. 4, flight controller 404 may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Further, flight controller may communicate withone or more additional devices as described below in further detail viaa network interface device. The network interface device may be utilizedfor commutatively connecting a flight controller to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. The network may include anynetwork topology and can may employ a wired and/or a wireless mode ofcommunication.

In an embodiment, and still referring to FIG. 4, flight controller 404may include, but is not limited to, for example, a cluster of flightcontrollers in a first location and a second flight controller orcluster of flight controllers in a second location. Flight controller404 may include one or more flight controllers dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. Flight controller 404 may be configured to distribute one or morecomputing tasks as described below across a plurality of flightcontrollers, which may operate in parallel, in series, redundantly, orin any other manner used for distribution of tasks or memory betweencomputing devices. For example, and without limitation, flightcontroller 404 may implement a control algorithm to distribute and/orcommand the plurality of flight controllers. As used in this disclosurea “control algorithm” is a finite sequence of well-defined computerimplementable instructions that may determine the flight component ofthe plurality of flight components to be adjusted. For example, andwithout limitation, control algorithm may include one or more algorithmsthat reduce and/or prevent aviation asymmetry. As a further non-limitingexample, control algorithms may include one or more models generated asa function of a software including, but not limited to Simulink byMathWorks, Natick, Mass., USA. In an embodiment, and without limitation,control algorithm may be configured to generate an auto-code, wherein an“auto-code,” is used herein, is a code and/or algorithm that isgenerated as a function of the one or more models and/or software's. Inanother embodiment, control algorithm may be configured to produce asegmented control algorithm. As used in this disclosure a “segmentedcontrol algorithm” is control algorithm that has been separated and/orparsed into discrete sections. For example, and without limitation,segmented control algorithm may parse control algorithm into two or moresegments, wherein each segment of control algorithm may be performed byone or more flight controllers operating on distinct flight components.

In an embodiment, and still referring to FIG. 4, control algorithm maybe configured to determine a segmentation boundary as a function ofsegmented control algorithm. As used in this disclosure a “segmentationboundary” is a limit and/or delineation associated with the segments ofthe segmented control algorithm. For example, and without limitation,segmentation boundary may denote that a segment in the control algorithmhas a first starting section and/or a first ending section. As a furthernon-limiting example, segmentation boundary may include one or moreboundaries associated with an ability of flight component 432. In anembodiment, control algorithm may be configured to create an optimizedsignal communication as a function of segmentation boundary. Forexample, and without limitation, optimized signal communication mayinclude identifying the discrete timing required to transmit and/orreceive the one or more segmentation boundaries. In an embodiment, andwithout limitation, creating optimized signal communication furthercomprises separating a plurality of signal codes across the plurality offlight controllers. For example, and without limitation the plurality offlight controllers may include one or more formal networks, whereinformal networks transmit data along an authority chain and/or arelimited to task-related communications. As a further non-limitingexample, communication network may include informal networks, whereininformal networks transmit data in any direction. In an embodiment, andwithout limitation, the plurality of flight controllers may include achain path, wherein a “chain path,” as used herein, is a linearcommunication path comprising a hierarchy that data may flow through. Inan embodiment, and without limitation, the plurality of flightcontrollers may include an all-channel path, wherein an “all-channelpath,” as used herein, is a communication path that is not restricted toa particular direction. For example, and without limitation, data may betransmitted upward, downward, laterally, and the like thereof. In anembodiment, and without limitation, the plurality of flight controllersmay include one or more neural networks that assign a weighted value toa transmitted datum. For example, and without limitation, a weightedvalue may be assigned as a function of one or more signals denoting thata flight component is malfunctioning and/or in a failure state.

Still referring to FIG. 4, the plurality of flight controllers mayinclude a master bus controller. As used in this disclosure a “masterbus controller” is one or more devices and/or components that areconnected to a bus to initiate a direct memory access transaction,wherein a bus is one or more terminals in a bus architecture. Master buscontroller may communicate using synchronous and/or asynchronous buscontrol protocols. In an embodiment, master bus controller may includeflight controller 404. In another embodiment, master bus controller mayinclude one or more universal asynchronous receiver-transmitters (UART).For example, and without limitation, master bus controller may includeone or more bus architectures that allow a bus to initiate a directmemory access transaction from one or more buses in the busarchitectures. As a further non-limiting example, master bus controllermay include one or more peripheral devices and/or components tocommunicate with another peripheral device and/or component and/or themaster bus controller. In an embodiment, master bus controller may beconfigured to perform bus arbitration. As used in this disclosure “busarbitration” is method and/or scheme to prevent multiple buses fromattempting to communicate with and/or connect to master bus controller.For example and without limitation, bus arbitration may include one ormore schemes such as a small computer interface system, wherein a smallcomputer interface system is a set of standards for physical connectingand transferring data between peripheral devices and master buscontroller by defining commands, protocols, electrical, optical, and/orlogical interfaces. In an embodiment, master bus controller may receiveintermediate representation 412 and/or output language from logiccomponent 420, wherein output language may include one or moreanalog-to-digital conversions, low bit rate transmissions, messageencryptions, digital signals, binary signals, logic signals, analogsignals, and the like thereof described above in detail.

Still referring to FIG. 4, master bus controller may communicate with aslave bus. As used in this disclosure a “slave bus” is one or moreperipheral devices and/or components that initiate a bus transfer. Forexample, and without limitation, slave bus may receive one or morecontrols and/or asymmetric communications from master bus controller,wherein slave bus transfers data stored to master bus controller. In anembodiment, and without limitation, slave bus may include one or moreinternal buses, such as but not limited to a/an internal data bus,memory bus, system bus, front-side bus, and the like thereof. In anotherembodiment, and without limitation, slave bus may include one or moreexternal buses such as external flight controllers, external computers,remote devices, printers, aircraft computer systems, flight controlsystems, and the like thereof.

In an embodiment, and still referring to FIG. 4, control algorithm mayoptimize signal communication as a function of determining one or morediscrete timings. For example, and without limitation master buscontroller may synchronize timing of the segmented control algorithm byinjecting high priority timing signals on a bus of the master buscontrol. As used in this disclosure a “high priority timing signal” isinformation denoting that the information is important. For example, andwithout limitation, high priority timing signal may denote that asection of control algorithm is of high priority and should be analyzedand/or transmitted prior to any other sections being analyzed and/ortransmitted. In an embodiment, high priority timing signal may includeone or more priority packets. As used in this disclosure a “prioritypacket” is a formatted unit of data that is communicated between theplurality of flight controllers. For example, and without limitation,priority packet may denote that a section of control algorithm should beused and/or is of greater priority than other sections.

Still referring to FIG. 4, flight controller 404 may also be implementedusing a “shared nothing” architecture in which data is cached at theworker, in an embodiment, this may enable scalability of aircraft and/orcomputing device. Flight controller 404 may include a distributer flightcontroller. As used in this disclosure a “distributer flight controller”is a component that adjusts and/or controls a plurality of flightcomponents as a function of a plurality of flight controllers. Forexample, distributer flight controller may include a flight controllerthat communicates with a plurality of additional flight controllersand/or clusters of flight controllers. In an embodiment, distributedflight control may include one or more neural networks. For example,neural network also known as an artificial neural network, is a networkof “nodes,” or data structures having one or more inputs, one or moreoutputs, and a function determining outputs based on inputs. Such nodesmay be organized in a network, such as without limitation aconvolutional neural network, including an input layer of nodes, one ormore intermediate layers, and an output layer of nodes. Connectionsbetween nodes may be created via the process of “training” the network,in which elements from a training dataset are applied to the inputnodes, a suitable training algorithm (such as Levenberg-Marquardt,conjugate gradient, simulated annealing, or other algorithms) is thenused to adjust the connections and weights between nodes in adjacentlayers of the neural network to produce the desired values at the outputnodes. This process is sometimes referred to as deep learning.

Still referring to FIG. 4, a node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above. In anembodiment, and without limitation, a neural network may receivesemantic units as inputs and output vectors representing such semanticunits according to weights w_(i) that are derived using machine-learningprocesses as described in this disclosure.

Still referring to FIG. 4, flight controller may include asub-controller 440. As used in this disclosure a “sub-controller” is acontroller and/or component that is part of a distributed controller asdescribed above; for instance, flight controller 404 may be and/orinclude a distributed flight controller made up of one or moresub-controllers. For example, and without limitation, sub-controller 440may include any controllers and/or components thereof that are similarto distributed flight controller and/or flight controller as describedabove. Sub-controller 440 may include any component of any flightcontroller as described above. Sub-controller 440 may be implemented inany manner suitable for implementation of a flight controller asdescribed above. As a further non-limiting example, sub-controller 440may include one or more processors, logic components and/or computingdevices capable of receiving, processing, and/or transmitting dataacross the distributed flight controller as described above. As afurther non-limiting example, sub-controller 440 may include acontroller that receives a signal from a first flight controller and/orfirst distributed flight controller component and transmits the signalto a plurality of additional sub-controllers and/or flight components.

Still referring to FIG. 4, flight controller may include a co-controller444. As used in this disclosure a “co-controller” is a controller and/orcomponent that joins flight controller 404 as components and/or nodes ofa distributer flight controller as described above. For example, andwithout limitation, co-controller 444 may include one or morecontrollers and/or components that are similar to flight controller 404.As a further non-limiting example, co-controller 444 may include anycontroller and/or component that joins flight controller 404 todistributer flight controller. As a further non-limiting example,co-controller 444 may include one or more processors, logic componentsand/or computing devices capable of receiving, processing, and/ortransmitting data to and/or from flight controller 404 to distributedflight control system. Co-controller 444 may include any component ofany flight controller as described above. Co-controller 444 may beimplemented in any manner suitable for implementation of a flightcontroller as described above.

In an embodiment, and with continued reference to FIG. 4, flightcontroller 404 may be designed and/or configured to perform any method,method step, or sequence of method steps in any embodiment described inthis disclosure, in any order and with any degree of repetition. Forinstance, flight controller 404 may be configured to perform a singlestep or sequence repeatedly until a desired or commanded outcome isachieved; repetition of a step or a sequence of steps may be performediteratively and/or recursively using outputs of previous repetitions asinputs to subsequent repetitions, aggregating inputs and/or outputs ofrepetitions to produce an aggregate result, reduction or decrement ofone or more variables such as global variables, and/or division of alarger processing task into a set of iteratively addressed smallerprocessing tasks. Flight controller may perform any step or sequence ofsteps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like; division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Referring now to FIG. 5, an exemplary embodiment of a machine-learningmodule 500 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module 500may be implemented in the determination of the flight states of theelectric aircraft. Machine-learning module 500 may communicated with theflight controller to determine a minimal drag axis for the electricaircraft. Machine-learning module may perform determinations,classification, and/or analysis steps, methods, processes, or the likeas described in this disclosure using machine learning processes. A“machine learning process,” as used in this disclosure, is a processthat automatedly uses training data 504 to generate an algorithm thatwill be performed by a computing device/module to produce outputs 508given data provided as inputs 512; this is in contrast to a non-machinelearning software program where the commands to be executed aredetermined in advance by a user and written in a programming language.

Still referring to FIG. 5, “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 504 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 504 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 504 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 504 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 504 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 504 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data504 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 5,training data 504 may include one or more elements that are notcategorized; that is, training data 504 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 504 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 504 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 504 used by machine-learning module 500 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample flight elements and/or pilot signals may be inputs, wherein anoutput may be an autonomous function.

Further referring to FIG. 5, training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 516. Training data classifier 516 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 500 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 504. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 516 may classify elements of training data tosub-categories of flight elements such as torques, forces, thrusts,directions, and the like thereof.

Still referring to FIG. 5, machine-learning module 500 may be configuredto perform a lazy-learning process 520 and/or protocol, which mayalternatively be referred to as a “lazy loading” or “call-when-needed”process and/or protocol, may be a process whereby machine learning isconducted upon receipt of an input to be converted to an output, bycombining the input and training set to derive the algorithm to be usedto produce the output on demand. For instance, an initial set ofsimulations may be performed to cover an initial heuristic and/or “firstguess” at an output and/or relationship. As a non-limiting example, aninitial heuristic may include a ranking of associations between inputsand elements of training data 504. Heuristic may include selecting somenumber of highest-ranking associations and/or training data 504elements. Lazy learning may implement any suitable lazy learningalgorithm, including without limitation a K-nearest neighbors algorithm,a lazy naïve Bayes algorithm, or the like; persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variouslazy-learning algorithms that may be applied to generate outputs asdescribed in this disclosure, including without limitation lazy learningapplications of machine-learning algorithms as described in furtherdetail below.

Alternatively or additionally, and with continued reference to FIG. 5,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 524. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 524 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 524 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 504set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 5, machine-learning algorithms may include atleast a supervised machine-learning process 528. At least a supervisedmachine-learning process 528, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude flight elements and/or pilot signals as described above asinputs, autonomous functions as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweeninputs and outputs; scoring function may, for instance, seek to maximizethe probability that a given input and/or combination of elements inputsis associated with a given output to minimize the probability that agiven input is not associated with a given output. Scoring function maybe expressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data 504. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of at least a supervised machine-learning process528 that may be used to determine relation between inputs and outputs.Supervised machine-learning processes may include classificationalgorithms as defined above.

Further referring to FIG. 5, machine learning processes may include atleast an unsupervised machine-learning processes 532. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 5, machine-learning module 500 may be designedand configured to create a machine-learning model 524 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 5, machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random-access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

Now referring to FIG. 6, a method 600 for propulsor management of anelectric aircraft is presented. At step 605, a state of transition of anelectric aircraft configured to transition between a hover state and afixed-wing state is determined by a flight controller; this may beimplemented, without limitation, as described above in reference toFIGS. 1-5. In some embodiments, the electric aircraft has a plurality ofpropulsors. In some embodiments, the plurality of propulsors includesvertical propulsors. In some embodiments, the electric aircraft is aneVTOL. In some embodiments, the hover state may be a state in which theelectric aircraft moves along a vertical path. In some embodiments, thismay include a vertical takeoff, a vertical landing, or maintaining analtitude while airborne. In some embodiments, the fixed-wing flightstate may include edgewise flight. In some embodiments, fixed-wingflight may include movement of the electric aircraft through the airforwards, backwards, and sideways. In some embodiments, the flightcontroller may determine a state of transition of the electric aircraftthrough a sensor. In some embodiments, the sensor may include motion,altitude, wind direction, pressure, or other sensors. In someembodiments, the flight controller may be configured to receive datafrom the sensor. In some embodiments, the flight controller may processthe data to determine if a transition to a different state is needed. Insome embodiments, the flight controller may determine if a transition ofstates is needed based on sensor data, flight plans, automated plans,and/or flight maneuver characteristics such as stall.

At step 610, the flight controller sends a parking command to theplurality of propulsors. The parking command may be sent through a wiredor wireless connection. This may be implemented, without limitation, asdescribed above in reference to FIGS. 1-5. The parking command mayinclude a command to position the plurality of propulsors to align withan axis of minimized drag. In some embodiments, the flight controllermay send an unparking command to enable the plurality of propulsors tofreely rotate. In some embodiments, the parking command may include datato align the plurality of propulsors with a direction of airflow inorder to reduce drag.

At step 615 the plurality of propulsors are moved using a first set ofinverters into a parked position. This may be implemented, withoutlimitation, as described above in reference to FIGS. 1-5. In someembodiments, the first set of inverters may be configured to apply atorque to the plurality of propulsors when the parking command isreceived. In other embodiments, there may be a second set of inverters.The second set of inverters may be configured to apply a torque to theplurality of propulsors when the parking command is received. In someembodiments, the first and second set of inverters may transform DC intoAC power. In some embodiments, the first set of inverters may hold atleast one propulsor of the plurality of propulsors in place. In someembodiments, the second set of inverters may apply a torque to at leastone propulsor of the plurality of propulsors. In some embodiments, thetorque applied is zero.

FIG. 7 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 700 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 700 includes a processor 704 and a memory708 that communicate with each other, and with other components, via abus 712. Bus 712 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 704 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 704 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 704 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating-pointunit (FPU), and/or system on a chip (SoC).

Memory 708 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 716 (BIOS), including basic routines that help totransfer information between elements within computer system 700, suchas during start-up, may be stored in memory 708. Memory 708 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 720 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 708 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 700 may also include a storage device 724. Examples of astorage device (e.g., storage device 724) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 724 may be connected to bus 712 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 724 (or one or morecomponents thereof) may be removably interfaced with computer system 700(e.g., via an external port connector (not shown)). Particularly,storage device 724 and an associated machine-readable medium 728 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 700. In one example, software 720 may reside, completelyor partially, within machine-readable medium 728. In another example,software 720 may reside, completely or partially, within processor 704.

Computer system 700 may also include an input device 732. In oneexample, a user of computer system 700 may enter commands and/or otherinformation into computer system 700 via input device 732. Examples ofan input device 732 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 732may be interfaced to bus 712 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 712, and any combinations thereof. Input device 732 mayinclude a touch screen interface that may be a part of or separate fromdisplay 736, discussed further below. Input device 732 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 700 via storage device 724 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 740. A network interfacedevice, such as network interface device 740, may be utilized forconnecting computer system 700 to one or more of a variety of networks,such as network 744, and one or more remote devices 748 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 744,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 720,etc.) may be communicated to and/or from computer system 700 via networkinterface device 740.

Computer system 700 may further include a video display adapter 752 forcommunicating a displayable image to a display device, such as displaydevice 736. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 752 and display device 736 may be utilized incombination with processor 704 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 700 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 712 via a peripheral interface 756. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for propulsion management of an electricaircraft, the system comprising: an electric aircraft configured totransition between a hover state and a fixed-wing flight state; aplurality of propulsors coupled to the electric aircraft, wherein afirst propulsor of the plurality of propulsors includes a first inverterand a second inverter; and a flight controller coupled to the electricaircraft, wherein the flight controller is configured to detect a statetransition of the electric aircraft from the hover state to thefixed-wing flight state and send a parking command to the plurality ofpropulsors, and wherein: the parking command causes the first inverterto move the first propulsor to a parked position; and the parkingcommand causes the second inverter to hold the first propulsor in theparked position.
 2. The system of claim 1, wherein the electric aircraftincludes an electric vertical takeoff and landing (eVTOL) aircraft. 3.The system of claim 1, wherein a minimum drag axis of each propulsor ofthe plurality of propulsors is configured to align with a direction ofairflow when receiving the parking command.
 4. The system of claim 1,wherein a minimum drag axis of each propulsor of the plurality ofpropulsors is configured to align with a longitudinal axis of theelectric aircraft when receiving the parking command.
 5. The system ofclaim 1, wherein the flight controller is configured to send the parkingcommand to park at least the first propulsor of the plurality ofpropulsors to be aligned with a longitudinal axis or airspeed directionwithin a 2-degree tolerance level.
 6. The system of claim 1, wherein atleast the first propulsor of the plurality of propulsors furthercomprises: an axis of rotation; a first blade having a first distal endand a first proximal end, wherein the first distal end is farther fromthe axis of rotation than the first proximal end; a second bladepositioned opposite the first blade, the second blade having a seconddistal end and a second proximal end, wherein the second distal end isfarther from the axis of rotation than the second proximal end.
 7. Thesystem of claim 6, wherein the at least a first propulsor has a minimaldrag axis running from the first distal end to the second distal end. 8.The system of claim 1, wherein the flight controller is configured todetect a transition of the electric aircraft from the fixed-wing flightstate to the hover state.
 9. The system of claim 8, wherein the flightcontroller is configured to send a command to at least the firstpropulsor of the plurality of propulsors to unpark.
 10. A method ofpropulsor management of an electric aircraft, the method comprising:determining, by a flight controller coupled to an electric aircraft, astate transition of the electric aircraft between a hover state of theelectric aircraft and a fixed-wing flight state of the electricaircraft, wherein the electric aircraft includes a plurality ofpropulsors coupled to the electric aircraft, and wherein a firstpropulsor of the plurality of propulsors includes a first inverter and asecond inverter; sending, by the flight controller, a parking command tothe plurality of propulsors; moving, as a function of the parkingcommand, using a first inverter, the first propulsor to a parkedposition; and holding, as a function of the parking command, using asecond inverter, the first propulsor in the parked position.
 11. Themethod of claim 10, wherein the electric aircraft includes an electricvertical takeoff and landing (eVTOL) aircraft.
 12. The method of claim10, wherein the first inverter is configured to apply a torque to apropulsor of the plurality of propulsors when the parking command isreceived from the flight controller.
 13. The method of claim 10, whereinthe flight controller is configured to sense and determine an optimalalignment of drag reduction of the electric aircraft.
 14. The system ofclaim 10, wherein at least the first propulsor of the plurality ofpropulsors is aligned with a minimal drag axis of a direction ofairflow.
 15. The system of claim 10, wherein at least the firstpropulsor of the plurality of propulsors is aligned with a longitudinalaxis of the electric aircraft.
 16. The system of claim 10, wherein atleast the first propulsor of the plurality of propulsors furthercomprises two blades positioned opposite one another.
 17. The system ofclaim 16, wherein a minimal drag axis aligns along a path from a firstdistal end of a first blade of the two blades to a second distal end ofa second blade of the two blades.
 18. The system of claim 10, whereinthe system further comprises a machine-learning module in communicationwith the flight controller.
 19. The system of claim 18, wherein themachine-learning module is configured to determine at least one of thehover state and the fixed-wing flight state of the electric aircraft.20. The system of claim 18, wherein the machine-learning module isconfigured to determine a minimal drag axis of the electric aircraft.